A neural model for heading detection from optic flow

This paper describes a neural model developed for computing heading from optic flow caused by 3D translational egomotion. The model uses the distributed representation of optic flow directions in cortical areas MT and MSTd. Model MSTd cells are selective for specific directions of visual motion and have large receptive fields covering approximately a quarter of the visual field at different retinal positions. The estimation of heading is computed in a polar framework by combining the activation of all MSTd cells in a geometrically motivated and biological plausible manner. In our implementation optic flow fields were generated from motion of a simulated camera in a static environment. We analysed the detection error by comparing estimated heading with the ground truth defined by the given camera motion. The results show that the described neural approach provides a robust detection method. We demonstrate that movements inducing radial flow patterns (forward movements) are detected more accurately than motions inducing laminar flow fields (e.g. sideward movements), consistent with psychophysical findings. Most important is that the described properties are a consequence of simple geometrical constraints defined by the spatial arrangement of MSTd cells.